Comments (1)
ShunLu91
commented on September 26, 2024
这确实是一个好问题，但由于资源不太充足以及re-train超参设置的公平性问题个人没有经验，所以re-train这一步我就没有继续进行（因为在cifar-10数据集很少看到有基于mobilenet/shufflenet block的网络进行re-train，SPOS原文也没有这样的实验结果，所以没有可参考的基准）
但是你问的这个问题可以从ImageNet数据集去评估，官方的代码以及论文中，给出了使用SPOS在ImageNet数据集搜索得到最优子结构train from scratch的最高精度是可以超过原始ShuffleNet精度的
from single-path-one-shot-nas.
Related Issues (15)
- Why do i maintain a 10% accuracy rate when training this model? HOT 4
- 采样出一条路径后，需要对其他的路径冻结梯度更新吗？ HOT 2
- 根据经验，cifar10和imagenet对应的哪些超参需要调整？ HOT 1
- Randomly sample and save models HOT 4
- 运行完 supernet.py 后，如何进行模型加载校验(val)？ HOT 4
- BN校准 HOT 2
- How to do channel search? HOT 1
- 请问下，在block selection时，不同的branch之间会共享权重么？ HOT 1
- model test HOT 1
- net output dimension mismatch on cifar10 HOT 7
- about extracting final result HOT 2
- 您好，关于精度复现 HOT 3
- Can not find cifar_train.py HOT 2
- Why shuffle_channels function is not used? HOT 2
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from single-path-one-shot-nas.